Reynolds Reed M, Popova Lucy, Yang Bo, Louviere Jordan, Thrasher James F
Communication Department, University of Massachusetts, Boston, MA, United States.
School of Public Health, Georgia State University, Atlanta, GA, United States.
Front Commun (Lausanne). 2025;10. doi: 10.3389/fcomm.2025.1385422. Epub 2025 Feb 17.
Experiments are widely used in communication research to help establish cause and effect, however, studies published in communication journals rarely use discrete choice experiments (DCEs). DCEs have become a mainstay in fields such as behavioral economics, medicine, and public policy, and can be used to enhance research on the effects of message attributes across a wide range of domains and modalities. DCEs are powerful for disentangling the influence of many message attributes with modest sample sizes and participant burden. The benefits of DCEs result from multiple design elements including stimulus sets that elicit direct comparisons, blocked and/or fractional factorial structures, and a wide range of analytic options. Though sophisticated, the tools necessary to implement a DCE are freely available, and this article provides resources to communication scholars and practitioners seeking to add DCEs to their own methodological repertoire.
实验在传播学研究中被广泛用于帮助确定因果关系,然而,发表在传播学期刊上的研究很少使用离散选择实验(DCEs)。DCEs已成为行为经济学、医学和公共政策等领域的支柱方法,可用于加强对广泛领域和模式中信息属性影响的研究。DCEs在以适度的样本量和参与者负担来理清许多信息属性的影响方面很强大。DCEs的优势源于多个设计元素,包括引发直接比较的刺激集、分块和/或分数析因结构,以及广泛的分析选项。尽管实施DCE所需的工具很复杂,但都是免费可得的,本文为寻求将DCEs添加到自己方法库中的传播学学者和从业者提供了资源。